Works in the field of neuroscience show that the human cortex is likely made up of columnar modules with reciprocal ascending and descending computational pathways. The human cortex contains about 30 billion neurons organized into hypercolumns, with each hypercolumn including about 100 to 1,000 minicolumns with diameters of 500 μm to 1 mm. Each minicolumn includes about 80 to 100 pyramidal neurons with diameters of 30 μm to 50 μm. It has been proposed that the columnar modules possess a computational architecture that performs the following functions: computes an abstraction from input data received from lower modules; projects a reconstruction from the module's deep neurons to the lower modules that best fits the input data from the lower modules; and projects from the module's superficial neurons to higher modules residual information that lies between the module's abstraction and a higher reconstruction of the module's concept received from the higher modules. It has also been theorized that concepts are memorized in attractor networks in assemblies of “all-or-none” sparsely activated neurons.
Multiple cognitive architectures have been proposed that computationally emulate pattern recognition. Existing cognitive architectures do not, however, emulate human cognition using concept states with correlated dimensions and conceptually constrained expectation states generated from correlations among dimensions of input states. Instead, existing architectures perform pattern recognition to distinguish and label learned patterns or to predict temporal patterns. Several existing architectures are based on neural networks that perform model-free computation based on unsupervised learning. The Hierarchical Temporal Memory (HTM) architecture proposed by Numenta of California, USA, includes a neural network that learns and predicts sparsely distributed patterns based on hierarchical regions. Each hierarchical region includes an array of columns of cells (emulating layer 3 neurons) that perform spatial and temporal prediction of the input in the ascending direction. The so-called “deep learning architecture” includes a deep hierarchy that is used for ascending pattern recognition. The deep hierarchy has many hidden layers, including an associative memory at the top. The HMAX algorithm uses a network of alternating layers of simple cells that perform weighted sum computation and complex cells that perform soft-max computation for ascending pattern recognition. Other cognitive architectures rely on symbolic processing that emulates higher-level cognition, but are limited by manual modeling and programming of computational rules. A cognitive architecture is sought that does not rely on existing pattern recognition architectures and that does not require programming of rules, manual modeling or semantic labeling of learned concepts but yet that can perform unsupervised learning and imprinting of conceptualized expectation knowledge.